I have two lines:
y = -1/3x + 4
y = 3x + 85
The intersection is at [24.3, 12.1].
I have a set of coordinates prepared:
points = [[1, 3], [4, 8], [25, 10], ... ]
#y = -1/3x + b
m_regr = -1/3
b_regr = 4
m_perp = 3 #(1 / m_regr * -1)
distances = []
points.each do |pair|
x1 = pair.first
y2 = pair.last
x2 = ((b_perp - b_regr / (m_regr - m_perp))
y2 = ((m_regr * b_perp) / (m_perp * b_regr))/(m_regr - m_perp)
distance = Math.hypot((y2 - y1), (x2 - x1))
distances << distance
end
Is there a gem or some better method for this?
NOTE: THE ABOVE METHOD DOES NOT WORK. See my answer for a solution that works.
What's wrong with using a little math?
If you have:
y = m1 x + b1
y = m2 x + b2
It's a simple system of linear equations.
If you solve them, your intersection is:
x = (b2 - b1)/(m1 - m2)
y = (m1 b2 - m2 b1)/(m1 - m2)
After much suffering and many different tries, I found a simple algebraic method here that not only works but is dramatically simplified.
distance = ((y - mx - b).abs / Math.sqrt(m**2 + 1))
where x and y are the coordinates for the known point.
For Future Googlers:
def solution k, l, m, n, p, q, r, s
intrsc_x1 = m - k
intrsc_y1 = n - l
intrsc_x2 = r - p
intrsc_y2 = s - q
v1 = (-intrsc_y1 * (k - p) + intrsc_x1 * (l - q)) / (-intrsc_x2 * intrsc_y1 + intrsc_x1 * intrsc_y2);
v2 = ( intrsc_x2 * (l - q) - intrsc_y2 * (k - p)) / (-intrsc_x2 * intrsc_y1 + intrsc_x1 * intrsc_y2);
(v1 >= 0 && v1 <= 1 && v2 >= 0 && v2 <= 1) ? true : false
end
The simplest and cleanest way I've found on the internet.
Related
I am running this code but it shows a method error. Can someone please help me?
Code:
function lsmc_am_put(S, K, r, σ, t, N, P)
Δt = t / N
R = exp(r * Δt)
T = typeof(S * exp(-σ^2 * Δt / 2 + σ * √Δt * 0.1) / R)
X = Array{T}(N+1, P)
for p = 1:P
X[1, p] = x = S
for n = 1:N
x *= R * exp(-σ^2 * Δt / 2 + σ * √Δt * randn())
X[n+1, p] = x
end
end
V = [max(K - x, 0) / R for x in X[N+1, :]]
for n = N-1:-1:1
I = V .!= 0
A = [x^d for d = 0:3, x in X[n+1, :]]
β = A[:, I]' \ V[I]
cV = A' * β
for p = 1:P
ev = max(K - X[n+1, p], 0)
if I[p] && cV[p] < ev
V[p] = ev / R
else
V[p] /= R
end
end
end
return max(mean(V), K - S)
end
lsmc_am_put(100, 90, 0.05, 0.3, 180/365, 1000, 10000)
error:
MethodError: no method matching (Array{Float64})(::Int64, ::Int64)
Closest candidates are:
(Array{T})(::LinearAlgebra.UniformScaling, ::Integer, ::Integer) where T at /Volumes/Julia-1.8.3/Julia-1.8.app/Contents/Resources/julia/share/julia/stdlib/v1.8/LinearAlgebra/src/uniformscaling.jl:508
(Array{T})(::Nothing, ::Any...) where T at baseext.jl:45
(Array{T})(::UndefInitializer, ::Int64) where T at boot.jl:473
...
Stacktrace:
[1] lsmc_am_put(S::Int64, K::Int64, r::Float64, σ::Float64, t::Float64, N::Int64, P::Int64)
# Main ./REPL[39]:5
[2] top-level scope
# REPL[40]:1
I tried this code and I was expecting a numeric answer but this error came up. I tried to look it up on google but I found nothing that matches my situation.
The error occurs where you wrote X = Array{T}(N+1, P). Instead, use one of the following approaches if you need a Vector:
julia> Array{Float64, 1}([1,2,3])
3-element Vector{Float64}:
1.0
2.0
3.0
julia> Vector{Float64}([1, 2, 3])
3-element Vector{Float64}:
1.0
2.0
3.0
And in your case, you should write X = Array{T,1}([N+1, P]) or X = Vector{T}([N+1, P]). But since there's such a X[1, p] = x = S expression in your code, I guess you mean to initialize a 2D array and update its elements through the algorithm. For this, you can define X like the following:
X = zeros(Float64, N+1, P)
# Or
X = Array{Float64, 2}(undef, N+1, P)
So, I tried the following in your code:
# I just changed the definition of `X` in your code like the following
X = Array{T, 2}(undef, N+1, P)
#And the result of the code was:
julia> lsmc_am_put(100, 90, 0.05, 0.3, 180/365, 1000, 10000)
3.329213731484463
I need to find the distance of O and N with the diagonales (with a 90° angle/ the shortest). I found a formula online, but why in this case, it does not return the good distance ?
And if possible, how to normalize the result (e.g. O is at20% of the diagonale?)
import numpy as np
import math
O = (1,3)
N = (3,2)
r = np.arange(24).reshape((6, 4))
def get_diagonal_distance(centroid, img_test):
x1, y1 = centroid
a, b = img.shape[1], img.shape[0]
c = np.sqrt(np.square(a) + np.square(b))
d = abs((a * x1 + b * y1 + c)) / (math.sqrt(a * a + b * b))
return d
print(f"diagonal d: {get_diagonal_distance(O, r): .4f}")
d = abs((a * x1 + b * y1 + c)) / (math.sqrt(a * a + b * b))
Your computation is wrong because a, b and c refer to the coefficients of the equation of the line ax+by+c=0
import numpy as np
O = (1,3)
N = (3,2)
M, L, I, H = (-1,-2), (3, -2), (3, 2), (-1, 2)
# Following your initial idea
def get_diagonal_distance(diagonal_extremes, point):
diagonal_vector = (diagonal_extremes[1][0] - diagonal_extremes[0][0],
diagonal_extremes[1][1] - diagonal_extremes[0][1])
a = diagonal_vector[1]
b = - diagonal_vector[0]
c = - diagonal_extremes[0][0]*a - diagonal_extremes[0][1]*b
x, y = point[0], point[1]
return abs((a * x + b * y + c)) / (np.sqrt(a * a + b * b))
# Taking advantage of numpy
def distance_from_diagonal(diagonal_extremes, point):
u = (diagonal_extremes[1][0] - diagonal_extremes[0][0],
diagonal_extremes[1][1] - diagonal_extremes[0][1])
v = (point[0] - diagonal_extremes[0][0],
point[1] - diagonal_extremes[0][1])
return np.cross(u, v) / np.linalg.norm(u)
print(f"diagonal d: {get_diagonal_distance((M, I), O): .4f}")
print(f"diagonal d: {distance_from_diagonal((M, I), O): .4f}")
Can I simply ask the logical flow of the below Mathematica code? What are the variables arg and abs doing? I have been searching for answers online and used ToMatlab but still cannot get the answer. Thank you.
Code:
PositiveCubicRoot[p_, q_, r_] :=
Module[{po3 = p/3, a, b, det, abs, arg},
b = ( po3^3 - po3 q/2 + r/2);
a = (-po3^2 + q/3);
det = a^3 + b^2;
If[det >= 0,
det = Power[Sqrt[det] - b, 1/3];
-po3 - a/det + det
,
(* evaluate real part, imaginary parts cancel anyway *)
abs = Sqrt[-a^3];
arg = ArcCos[-b/abs];
abs = Power[abs, 1/3];
abs = (abs - a/abs);
arg = -po3 + abs*Cos[arg/3]
]
]
abs and arg are being reused multiple times in the algorithm.
In a case where det > 0 the steps are
po3 = p/3;
b = (po3^3 - po3 q/2 + r/2);
a = (-po3^2 + q/3);
abs1 = Sqrt[-a^3];
arg1 = ArcCos[-b/abs1];
abs2 = Power[abs1, 1/3];
abs3 = (abs2 - a/abs2);
arg2 = -po3 + abs3*Cos[arg1/3]
abs3 can be identified as A in this answer: Using trig identity to a solve cubic equation
That is the most salient point of this answer.
Evaluating symbolically and numerically may provide some other insights.
Using demo inputs
{p, q, r} = {-2.52111798, -71.424692, -129.51520};
Copyable version of trig identity notes - NB a, b, p & q are used differently in this post
Plot[x^3 - 2.52111798 x^2 - 71.424692 x - 129.51520, {x, 0, 15}]
a = 1;
b = -2.52111798;
c = -71.424692;
d = -129.51520;
p = (3 a c - b^2)/3 a^2;
q = (2 b^3 - 9 a b c + 27 a^2 d)/27 a^3;
A = 2 Sqrt[-p/3]
A == abs3
-(b/3) + A Cos[1/3 ArcCos[
-((b/3)^3 - (b/3) c/2 + d/2)/Sqrt[-(-(b^2/9) + c/3)^3]]]
Edit
There is also a solution shown here
TRIGONOMETRIC SOLUTION TO THE CUBIC EQUATION, by Alvaro H. Salas
Clear[a, b, c]
1/3 (-a + 2 Sqrt[a^2 - 3 b] Cos[1/3 ArcCos[
(-2 a^3 + 9 a b - 27 c)/(2 (a^2 - 3 b)^(3/2))]]) /.
{a -> -2.52111798, b -> -71.424692, c -> -129.51520}
10.499
I have a data cube a of radius w and for every element of that cube, I would like to add the element and all surrounding values within a cube of radius r, where r < w. The result should be returned in an array of the same shape, b.
As a simple example, suppose:
a = numpy.ones(shape=(2*w,2*w,2*w),dtype='float32')
kernel = numpy.ones(shape=(2*r,2*r,2*r),dtype='float32')
b = convolve(a,kernel,mode='constant',cval=0)
then b would have the value (2r)(2r)(2r) for all the indices not on the edge.
Currently I am using a loop to do this and it is very slow, especially for larger w and r. I tried scipy convolution but got little speedup over the loop. I am now looking at numba's parallel computation feature but cannot figure out how to rewrite the code to work with numba. I have a Nvidia RTX card so CUDA GPU calculations are also possible.
Suggestions are welcome.
Here is my current code:
for x in range(0,w*2):
print(x)
for y in range(0,w*2):
for z in range(0,w*2):
if x >= r:
x1 = x - r
else:
x1 = 0
if x < w*2-r:
x2 = x + r
else:
x2 = w*2 - 1
if y >= r:
y1 = y - r
else:
y1 = 0
if y < w*2-r:
y2 = y + r
else:
y2 = w*2 - 1
if z >= r:
z1 = z - r
else:
z1 = 0
if z < w*2-r:
z2 = z + r
else:
z2 = w*2 - 1
b[x][y][z] = numpy.sum(a[x1:x2,y1:y2,z1:z2])
return b
Here is a very simple version of your code so that it works with numba. I was finding speed-ups of a factor of 10 relative to the pure numpy code. However, you should be able to get even greater speed-ups using a FFT convolution algorithm (e.g. scipy's fftconvolve). Can you share your attempt at getting convolution to work?
from numba import njit
#njit
def sum_cubes(a,b,w,r):
for x in range(0,w*2):
#print(x)
for y in range(0,w*2):
for z in range(0,w*2):
if x >= r:
x1 = x - r
else:
x1 = 0
if x < w*2-r:
x2 = x + r
else:
x2 = w*2 - 1
if y >= r:
y1 = y - r
else:
y1 = 0
if y < w*2-r:
y2 = y + r
else:
y2 = w*2 - 1
if z >= r:
z1 = z - r
else:
z1 = 0
if z < w*2-r:
z2 = z + r
else:
z2 = w*2 - 1
b[x,y,z] = np.sum(a[x1:x2,y1:y2,z1:z2])
return b
EDIT: Your original code has a small bug in it. The way numpy indexing works, the final line should be
b[x,y,z] = np.sum(a[x1:x2+1,y1:y2+1,z1:z2+1])
unless you want the cube to be off-centre.
Assuming you do want the cube to be centred, then a much faster way to do this calculation is using scipy's uniform filter:
from scipy.ndimage import uniform_filter
def sum_cubes_quickly(a,b,w,r):
b = uniform_filter(a,mode='constant',cval=0,size=2*r+1)*(2*r+1)**3
return b
A few quick runtime comparisons for randomly generated data with w = 50, r = 10:
Original raw numpy code - 15.1 sec
Numba'd numpy code - 8.1 sec
uniform_filter - 13.1 ms
A question last week defined the zig zag ordering on an n by m matrix and asked how to list the elements in that order.
My question is how to quickly find the ith item in the zigzag ordering? That is, without traversing the matrix (for large n and m that's much too slow).
For example with n=m=8 as in the picture and (x, y) describing (row, column)
f(0) = (0, 0)
f(1) = (0, 1)
f(2) = (1, 0)
f(3) = (2, 0)
f(4) = (1, 1)
...
f(63) = (7, 7)
Specific question: what is the ten billionth (1e10) item in the zigzag ordering of a million by million matrix?
Let's assume that the desired element is located in the upper half of the matrix. The length of the diagonals are 1, 2, 3 ..., n.
Let's find the desired diagonal. It satisfies the following property:
sum(1, 2 ..., k) >= pos but sum(1, 2, ..., k - 1) < pos. The sum of 1, 2, ..., k is k * (k + 1) / 2. So we just need to find the smallest integer k such that k * (k + 1) / 2 >= pos. We can either use a binary search or solve this quadratic inequality explicitly.
When we know the k, we just need to find the pos - (k - 1) * k / 2 element of this diagonal. We know where it starts and where we should move(up or down, depending on the parity of k), so we can find the desired cell using a simple formula.
This solution has an O(1) or an O(log n) time complexity(it depends on whether we use a binary search or solve the inequation explicitly in step 2).
If the desired element is located in the lower half of the matrix, we can solve this problem for a pos' = n * n - pos + 1 and then use symmetry to get the solution to the original problem.
I used 1-based indexing in this solution, using 0-based indexing might require adding +1 or -1 somewhere, but the idea of the solution is the same.
If the matrix is rectangular, not square, we need to consider the fact the length of diagonals look this way: 1, 2, 3, ..., m, m, m, .., m, m - 1, ..., 1(if m <= n) when we search for the k, so the sum becomes something like k * (k + 1) / 2 if k <= m and k * (k + 1) / 2 + m * (k - m) otherwise.
import math, random
def naive(n, m, ord, swap = False):
dx = 1
dy = -1
if swap:
dx, dy = dy, dx
cur = [0, 0]
for i in range(ord):
cur[0] += dy
cur[1] += dx
if cur[0] < 0 or cur[1] < 0 or cur[0] >= n or cur[1] >= m:
dx, dy = dy, dx
if cur[0] >= n:
cur[0] = n - 1
cur[1] += 2
if cur[1] >= m:
cur[1] = m - 1
cur[0] += 2
if cur[0] < 0: cur[0] = 0
if cur[1] < 0: cur[1] = 0
return cur
def fast(n, m, ord, swap = False):
if n < m:
x, y = fast(m, n, ord, not swap)
return [y, x]
alt = n * m - ord - 1
if alt < ord:
x, y = fast(n, m, alt, swap if (n + m) % 2 == 0 else not swap)
return [n - x - 1, m - y - 1]
if ord < (m * (m + 1) / 2):
diag = int((-1 + math.sqrt(1 + 8 * ord)) / 2)
parity = (diag + (0 if swap else 1)) % 2
within = ord - (diag * (diag + 1) / 2)
if parity: return [diag - within, within]
else: return [within, diag - within]
else:
ord -= (m * (m + 1) / 2)
diag = int(ord / m)
within = ord - diag * m
diag += m
parity = (diag + (0 if swap else 1)) % 2
if not parity:
within = m - within - 1
return [diag - within, within]
if __name__ == "__main__":
for i in range(1000):
n = random.randint(3, 100)
m = random.randint(3, 100)
ord = random.randint(0, n * m - 1)
swap = random.randint(0, 99) < 50
na = naive(n, m, ord, swap)
fa = fast(n, m, ord, swap)
assert na == fa, "(%d, %d, %d, %s) ==> (%s), (%s)" % (n, m, ord, swap, na, fa)
print fast(1000000, 1000000, 9999999999, False)
print fast(1000000, 1000000, 10000000000, False)
So the 10-billionth element (the one with ordinal 9999999999), and the 10-billion-first element (the one with ordinal 10^10) are:
[20331, 121089]
[20330, 121090]
An analytical solution
In the general case, your matrix will be divided in 3 areas:
an initial triangle t1
a skewed part mid where diagonals have a constant length
a final triangle t2
Let's call p the index of your diagonal run.
We want to define two functions x(p) and y(p) that give you the column and row of the pth cell.
Initial triangle
Let's look at the initial triangular part t1, where each new diagonal is one unit longer than the preceding.
Now let's call d the index of the diagonal that holds the cell, and
Sp = sum(di) for i in [0..p-1]
We have p = Sp + k, with 0 <=k <= d and
Sp = d(d+1)/2
if we solve for d, it brings
d²+d-2p = 0, a quadratic equation where we retain only the positive root:
d = (-1+sqrt(1+8*p))/2
Now we want the highest integer value closest to d, which is floor(d).
In the end, we have
p = d + k with d = floor((-1+sqrt(1+8*p))/2) and k = p - d(d+1)/2
Let's call
o(d) the function that equals 1 if d is odd and 0 otherwise, and
e(d) the function that equals 1 if d is even and 0 otherwise.
We can compute x(p) and y(p) like so:
d = floor((-1+sqrt(1+8*p))/2)
k = p - d(d+1)/2
o = d % 2
e = 1 - o
x = e*d + (o-e)*k
y = o*d + (e-o)*k
even and odd functions are used to try to salvage some clarity, but you can replace
e(p) with 1 - o(p) and have slightly more efficient but less symetric formulaes for x and y.
Middle part
let's consider the smallest matrix dimension s, i.e. s = min (m,n).
The previous formulaes hold until x or y (whichever comes first) reaches the value s.
The upper bound of p such as x(i) <= s and y(i) <= s for all i in [0..p]
(i.e. the cell indexed by p is inside the initial triangle t1) is given by
pt1 = s(s+1)/2.
For p >= pt1, diagonal length remains equal to s until we reach the second triangle t2.
when inside mid, we have:
p = s(s+1)/2 + ds + k with k in [0..s[.
which yields:
d = floor ((p - s(s+1)/2)/s)
k = p - ds
We can then use the same even/odd trick to compute x(p) and y(p):
p -= s(s+1)/2
d = floor (p / s)
k = p - d*s
o = (d+s) % 2
e = 1 - o
x = o*s + (e-o)*k
y = e*s + (o-e)*k
if (n > m)
x += d+e
y -= e
else
y += d+o
x -= o
Final triangle
Using symetry, we can calculate pt2 = m*n - s(s+1)/2
We now face nearly the same problem as for t1, except that the diagonal may run in the same direction as for t1 or in the reverse direction (if n+m is odd).
Using symetry tricks, we can compute x(p) and y(p) like so:
p = n*m -1 - p
d = floor((-1+sqrt(1+8*p))/2)
k = p - d*(d+1)/2
o = (d+m+n) % 2
e = 1 - $o;
x = n-1 - (o*d + (e-o)*k)
y = m-1 - (e*d + (o-e)*k)
Putting all together
Here is a sample c++ implementation.
I used 64 bits integers out of sheer lazyness. Most could be replaced by 32 bits values.
The computations could be made more effective by precomputing a few more coefficients.
A good part of the code could be factorized, but I doubt it is worth the effort.
Since this is just a quick and dirty proof of concept, I did not optimize it.
#include <cstdio> // printf
#include <algorithm> // min
using namespace std;
typedef long long tCoord;
void panic(const char * msg)
{
printf("PANIC: %s\n", msg);
exit(-1);
}
struct tPoint {
tCoord x, y;
tPoint(tCoord x = 0, tCoord y = 0) : x(x), y(y) {}
tPoint operator+(const tPoint & p) const { return{ x + p.x, y + p.y }; }
bool operator!=(const tPoint & p) const { return x != p.x || y != p.y; }
};
class tMatrix {
tCoord n, m; // dimensions
tCoord s; // smallest dimension
tCoord pt1, pt2; // t1 / mid / t2 limits for p
public:
tMatrix(tCoord n, tCoord m) : n(n), m(m)
{
s = min(n, m);
pt1 = (s*(s + 1)) / 2;
pt2 = n*m - pt1;
}
tPoint diagonal_cell(tCoord p)
{
tCoord x, y;
if (p < pt1) // inside t1
{
tCoord d = (tCoord)floor((-1 + sqrt(1 + 8 * p)) / 2);
tCoord k = p - (d*(d + 1)) / 2;
tCoord o = d % 2;
tCoord e = 1 - o;
x = o*d + (e - o)*k;
y = e*d + (o - e)*k;
}
else if (p < pt2) // inside mid
{
p -= pt1;
tCoord d = (tCoord)floor(p / s);
tCoord k = p - d*s;
tCoord o = (d + s) % 2;
tCoord e = 1 - o;
x = o*s + (e - o)*k;
y = e*s + (o - e)*k;
if (m > n) // vertical matrix
{
x -= o;
y += d + o;
}
else // horizontal matrix
{
x += d + e;
y -= e;
}
}
else // inside t2
{
p = n * m - 1 - p;
tCoord d = (tCoord)floor((-1 + sqrt(1 + 8 * p)) / 2);
tCoord k = p - (d*(d + 1)) / 2;
tCoord o = (d + m + n) % 2;
tCoord e = 1 - o;
x = n - 1 - (o*d + (e - o)*k);
y = m - 1 - (e*d + (o - e)*k);
}
return{ x, y };
}
void check(void)
{
tPoint move[4] = { { 1, 0 }, { -1, 1 }, { 1, -1 }, { 0, 1 } };
tPoint pos;
tCoord dir = 0;
for (tCoord p = 0; p != n * m ; p++)
{
tPoint dc = diagonal_cell(p);
if (pos != dc) panic("zot!");
pos = pos + move[dir];
if (dir == 0)
{
if (pos.y == m - 1) dir = 2;
else dir = 1;
}
else if (dir == 3)
{
if (pos.x == n - 1) dir = 1;
else dir = 2;
}
else if (dir == 1)
{
if (pos.y == m - 1) dir = 0;
else if (pos.x == 0) dir = 3;
}
else
{
if (pos.x == n - 1) dir = 3;
else if (pos.y == 0) dir = 0;
}
}
}
};
void main(void)
{
const tPoint dim[] = { { 10, 10 }, { 11, 11 }, { 10, 30 }, { 30, 10 }, { 10, 31 }, { 31, 10 }, { 11, 31 }, { 31, 11 } };
for (tPoint d : dim)
{
printf("Checking a %lldx%lld matrix...", d.x, d.y);
tMatrix(d.x, d.y).check();
printf("done\n");
}
tCoord p = 10000000000;
tMatrix matrix(1000000, 1000000);
tPoint cell = matrix.diagonal_cell(p);
printf("Coordinates of %lldth cell: (%lld,%lld)\n", p, cell.x, cell.y);
}
Results are checked against "manual" sweep of the matrix.
This "manual" sweep is a ugly hack that won't work for a one-row or one-column matrix, though diagonal_cell() does work on any matrix (the "diagonal" sweep becomes linear in that case).
The coordinates found for the 10.000.000.000th cell of a 1.000.000x1.000.000 matrix seem consistent, since the diagonal d on which the cell stands is about sqrt(2*1e10), approx. 141421, and the sum of cell coordinates is about equal to d (121090+20330 = 141420). Besides, it is also what the two other posters report.
I would say there is a good chance this lump of obfuscated code actually produces an O(1) solution to your problem.